Yihua Zhang


Room 3210

428 S Shaw LN

East Lansing, Michigan, USA

I am Yihua Zhang (张逸骅), a second-year Ph.D. student from OPTML Group at Michigan State University, supervised by Prof. Sijia Liu. My research focuses on the trustworthy and scalable ML algorithms. In general, my research spans the areas of machine learning (ML)/deep learning (DL), optimization theory, computer vision, and security. These research topics provide a solid foundation for my current and future research: Making AI system responsible and efficient. My research on these two goals are intervened and can be summarized as the following two perspectives:

:heavy_check_mark: Algorithmic perspective: This line of research designs the scalable and theoretically-grounded machine learning algorithms subject to real-life constraints, e.g., computation/communication overhead, robustness, fairness, and interpretability.

:heavy_check_mark: Application perspective: This line of research tackles the domain-specific challenges to achieve scalable and trustworthy AI, e.g., robustness enhancement, fairness promotion, data privacy protection, and model compression.


Jan 21, 2023 :tada: Two papers accepted in ICLR 2023, including one first-authored paper!
Dec 21, 2022 :tada: I was awarded the AAAI 2023 student scholarship !
Oct 22, 2022 :cancer: The video of the BiP paper is released on YouTube.
Oct 11, 2022 :tada: Our tutorial on “Bi-level Optimization in Machine Learning: Foundations and Applications” is accepted to AAAI 2023 !
Oct 8, 2022 :tada: I was selected to make the NeurIPS 2022 Top Reviewer List !

selected publications

  1. NeurIPS’22
    Advancing Model Pruning via Bi-level Optimization
    Yihua Zhang*, Yuguang Yao*, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, and Sijia Liu
    In Thirty-sixth Conference on Neural Information Processing Systems 2022
  2. NeurIPS’22
    Fairness Reprogramming
    Guanhua Zhang*, Yihua Zhang*, Yang Zhang, Wenqi Fan, Qing Li, Sijia Liu, and Shiyu Chang
    In Thirty-sixth Conference on Neural Information Processing Systems 2022
  3. UAI’22
    Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
    Gaoyuan Zhang*, Songtao Lu*, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, and Sijia Liu
    In Uncertainty in Artificial Intelligence 2022
  4. ICML’22
    Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
    Yihua Zhang*, Guanhua Zhang*, Prashant Khanduri, Mingyi Hong, Shiyu Chang, and Sijia Liu
    In Proceedings of the 39th International Conference on Machine Learning 2022
  5. CVPR’22
    Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free
    Tianlong Chen*, Zhenyu Zhang*, Yihua Zhang*, Shiyu Chang, Sijia Liu, and Zhangyang Wang
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022